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November 18 2013

aleshaespa

roofing contractors cardiff

When designing the roof, the things to be kept in mind are the purpose of the constructing the building, the general weather conditions of the place it stands in, the local traditions, architectural designing concepts, government regulations if any and availability of materials.
Reposted fromPoinom Poinom

November 05 2013

Data Driven Computing: The Future Fabric of Data Analysis

The nature of computing has changed dramatically over the last decade, and more innovation is needed to weather the gathering data storm.

 

When subatomic particles smash together at the Large Hadron Collider in Switzerland, they create showers of new particles whose signatures are recorded by four detectors. The LHC captures 5 trillion bits of data — more information than all of the world’s libraries combined — every second. After the judicious application of filtering algorithms, more than 99 percent of those data are discarded, but the four experiments still produce a whopping 25 petabytes (25×10E15 bytes) of data per year that must be stored and analyzed. That is a scale far beyond the computing resources of any single facility, so the LHC scientists rely on a vast computing grid of 160 data centers around the world, a distributed network that is capable of transferring as much as 10 gigabytes per second at peak performance.

 

Google’s Alon Halevy believes that the real breakthroughs in big data analysis are likely to come from integration — specifically, integrating across very different data sets. “No matter how much you speed up the computers or the way you put computers together, the real issues are at the data level,” he said. For example, a raw data set could include thousands of different tables scattered around the Web, each one listing crime rates in New York, but each may use different terminology and column headers, known as “schema.” A header of “New York” can describe the state, the five boroughs of New York City, or just Manhattan. You must understand the relationship between the schemas before the data in all those tables can be integrated.

 

That, in turn, requires breakthroughs in techniques to analyze the semantics of natural language. It is one of the toughest problems in artificial intelligence — if your machine-learning algorithm aspires to perfect understanding of nearly every word. But what if your algorithm needs to understand only enough of the surrounding text to determine whether, for example, a table includes data on coffee production in various countries so that it can then integrate the table with other, similar tables into one common data set? According to Halevy, a researcher could first use a coarse-grained algorithm to parse the underlying semantics of the data as best it could and then adopt a crowd-sourcing approach like a Mechanical Turk to refine the model further through human input. “The humans are training the system without realizing it, and then the system can answer many more questions based on what it has learned,” he said.

 

Chris Mattmann, a senior computer scientist at NASA’s Jet Propulsion Laboratory and director at the Apache Software Foundation, faces just such a complicated scenario with a research project that seeks to integrate two different sources of climate information: remote-sensing observations of the Earth made by satellite instrumentation and computer-simulated climate model outputs. The Intergovernmental Panel on Climate Change would like to be able to compare the various climate models against the hard remote-sensing data to determine which models provide the best fit. But each of those sources stores data in different formats, and there are many different versions of those formats.

 

Many researchers emphasize the need to develop a broad spectrum of flexible tools that can deal with many different kinds of data. For example, many users are shifting from traditional highly structured relational databases, broadly known as SQL, which represent data in a conventional tabular format, to a more flexible format dubbed NoSQL. “It can be as structured or unstructured as you need it to be,” said Matt LeMay, a product and communications consultant and the former head of consumer products at URL shortening and bookmarking service Bitly, which uses both SQL and NoSQL formats for data storage, depending on the application.



Data Driven Computing: The Future Fabric of Data Analysis

The nature of computing has changed dramatically over the last decade, and more innovation is needed to weather the gathering data storm.

 

When subatomic particles smash together at the Large Hadron Collider in Switzerland, they create showers of new particles whose signatures are recorded by four detectors. The LHC captures 5 trillion bits of data — more information than all of the world’s libraries combined — every second. After the judicious application of filtering algorithms, more than 99 percent of those data are discarded, but the four experiments still produce a whopping 25 petabytes (25×10E15 bytes) of data per year that must be stored and analyzed. That is a scale far beyond the computing resources of any single facility, so the LHC scientists rely on a vast computing grid of 160 data centers around the world, a distributed network that is capable of transferring as much as 10 gigabytes per second at peak performance.

 

Google’s Alon Halevy believes that the real breakthroughs in big data analysis are likely to come from integration — specifically, integrating across very different data sets. “No matter how much you speed up the computers or the way you put computers together, the real issues are at the data level,” he said. For example, a raw data set could include thousands of different tables scattered around the Web, each one listing crime rates in New York, but each may use different terminology and column headers, known as “schema.” A header of “New York” can describe the state, the five boroughs of New York City, or just Manhattan. You must understand the relationship between the schemas before the data in all those tables can be integrated.

 

That, in turn, requires breakthroughs in techniques to analyze the semantics of natural language. It is one of the toughest problems in artificial intelligence — if your machine-learning algorithm aspires to perfect understanding of nearly every word. But what if your algorithm needs to understand only enough of the surrounding text to determine whether, for example, a table includes data on coffee production in various countries so that it can then integrate the table with other, similar tables into one common data set? According to Halevy, a researcher could first use a coarse-grained algorithm to parse the underlying semantics of the data as best it could and then adopt a crowd-sourcing approach like a Mechanical Turk to refine the model further through human input. “The humans are training the system without realizing it, and then the system can answer many more questions based on what it has learned,” he said.

 

Chris Mattmann, a senior computer scientist at NASA’s Jet Propulsion Laboratory and director at the Apache Software Foundation, faces just such a complicated scenario with a research project that seeks to integrate two different sources of climate information: remote-sensing observations of the Earth made by satellite instrumentation and computer-simulated climate model outputs. The Intergovernmental Panel on Climate Change would like to be able to compare the various climate models against the hard remote-sensing data to determine which models provide the best fit. But each of those sources stores data in different formats, and there are many different versions of those formats.

 

Many researchers emphasize the need to develop a broad spectrum of flexible tools that can deal with many different kinds of data. For example, many users are shifting from traditional highly structured relational databases, broadly known as SQL, which represent data in a conventional tabular format, to a more flexible format dubbed NoSQL. “It can be as structured or unstructured as you need it to be,” said Matt LeMay, a product and communications consultant and the former head of consumer products at URL shortening and bookmarking service Bitly, which uses both SQL and NoSQL formats for data storage, depending on the application.



Five reasons why doing business in Singapore is so easy



Five reasons why doing business in Singapore is so easy



Graph Databases - Free eBook Share

eBook Free Download: Graph Databases | PDF, EPUB | ISBN: 1449356265 | 2013-06-14 | English | PutLocker


Graph Databases - Free eBook Share

eBook Free Download: Graph Databases | PDF, EPUB | ISBN: 1449356265 | 2013-06-14 | English | PutLocker


August 27 2013

aleshaespa
1579 42a5 390
Masami
Reposted fromerozupa erozupa
aleshaespa
1579 42a5 390
Masami
Reposted fromerozupa erozupa
aleshaespa

June 19 2013

Sharing Economy Money Panel - LeWeb London 2013

For established industries, the sharing marketplace — with rapidly shifting social, cultural, and technological disruptions — is forcing them to respond too. Never more true than in the financial sector. Does crowd funding threaten traditional funding sources indefinitely? We'll hear from the $$$ experts about the state of funding and financial models in The Sharing Economy.


Sharing Economy Money Panel - LeWeb London 2013

For established industries, the sharing marketplace — with rapidly shifting social, cultural, and technological disruptions — is forcing them to respond too. Never more true than in the financial sector. Does crowd funding threaten traditional funding sources indefinitely? We'll hear from the $$$ experts about the state of funding and financial models in The Sharing Economy.


Getting the Most out of Desk Space

Why desk space is important for an optimal workspace



Getting the Most out of Desk Space

Why desk space is important for an optimal workspace



WI: Healthy sprinkling of greed can reap economic growth | Milwaukee Journal Sentinel

In the world of economic development, the grass always seems greener someplace else.

 

Some people in Milwaukee think Madison is roaring ahead because of its head start in the tech-based economy — but the view from Madison is that Wisconsin's capital city isn't growing as fast as Austin, Lincoln, Des Moines or other U.S. techie hubs.

 

Some say Ohio is the model for stimulating a tech-based economy — still others proclaim Michigan ... or Utah ... or Maryland. It all depends on what patch of grass you're standing, it would seem.

 

No matter where you live, however, everyone points to California's Silicon Valley as a shining star of economic growth ... except for some of the business people who live and work there. They cite the region's high cost of living, the never-ending musical chair game for talent, state regulations and other factors contributing to a mini-exodus from the Valley.

 

Four Silicon Valley entrepreneurs and investors with Wisconsin ties returned to the state this month for the annual Wisconsin Entrepreneurs' Conference. Their message: Don't try to be Silicon Valley, but don't ignore what made it successful, either.

 

"Business isn't about staying busy; it's about making money," said Sanjeev Chitre, one of four University of Wisconsin-Madison engineering alumni who spoke at the conference. All are members of the Badger Entrepreneurship Forum, a San Francisco group that helps California start-ups with Wisconsin ties.

 

Chitre said Wisconsin needs to be infused with a healthy dose of "greed," meaning it should be more culturally acceptable here to pursue creation of personal wealth. That, he said, is ultimately the best way to spur even more economic growth. In California's Silicon Valley, "greed is good" and it's a powerful motivator.

 

Click headline to read more--

 



WI: Healthy sprinkling of greed can reap economic growth | Milwaukee Journal Sentinel

In the world of economic development, the grass always seems greener someplace else.

 

Some people in Milwaukee think Madison is roaring ahead because of its head start in the tech-based economy — but the view from Madison is that Wisconsin's capital city isn't growing as fast as Austin, Lincoln, Des Moines or other U.S. techie hubs.

 

Some say Ohio is the model for stimulating a tech-based economy — still others proclaim Michigan ... or Utah ... or Maryland. It all depends on what patch of grass you're standing, it would seem.

 

No matter where you live, however, everyone points to California's Silicon Valley as a shining star of economic growth ... except for some of the business people who live and work there. They cite the region's high cost of living, the never-ending musical chair game for talent, state regulations and other factors contributing to a mini-exodus from the Valley.

 

Four Silicon Valley entrepreneurs and investors with Wisconsin ties returned to the state this month for the annual Wisconsin Entrepreneurs' Conference. Their message: Don't try to be Silicon Valley, but don't ignore what made it successful, either.

 

"Business isn't about staying busy; it's about making money," said Sanjeev Chitre, one of four University of Wisconsin-Madison engineering alumni who spoke at the conference. All are members of the Badger Entrepreneurship Forum, a San Francisco group that helps California start-ups with Wisconsin ties.

 

Chitre said Wisconsin needs to be infused with a healthy dose of "greed," meaning it should be more culturally acceptable here to pursue creation of personal wealth. That, he said, is ultimately the best way to spur even more economic growth. In California's Silicon Valley, "greed is good" and it's a powerful motivator.

 

Click headline to read more--

 



June 16 2013

Finding the Perfect Housemaid



Finding the Perfect Housemaid



5 inspiring lessons from SXSW from David Sealey

SXSW provides plenty of insight and inspiration. Read David Sealey's top 5 inspiring notes for Marketers from SXSW 2013 at Smart Insights



5 inspiring lessons from SXSW from David Sealey

SXSW provides plenty of insight and inspiration. Read David Sealey's top 5 inspiring notes for Marketers from SXSW 2013 at Smart Insights



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